Generalized model for predicting the thermal conductivity of fine-grained soils

As a renewable energy, geothermal is considered as one of the important approaches to mitigate the energy crisis and climate change. The thermal conductivity of soils is a key parameter required in the utilization of geothermal resources, and it plays an important role in engineering, environmental science, earth science, and agriculture. Generally, the existing models that predict the thermal conductivity of soil are limited to specific types of soil and specific conditions. As a result, it is difficult to apply these models to engineering practice. Therefore, a simple, efficient, and generalized model capable of predicting the thermal conductivity of soils is necessary. In this work, a generalized model for calculating the thermal conductivity of fine-grained soils is established. This model considers the initial dry density, initial water content, temperature, and mineral composition. The proposed generalized model is based on a large amount of measured data from different countries and regions. Its validity is verified after being cross-validated with a back-propagation artificial neural network (BP-ANN) model. Compared with two existing models, the proposed model achieves the best thermal conductivity estimation of fine-grained soils. Additionally, it has a wide range of applications, requires only a few parameters, and does not need recalibration.